def __call__(self, sample: dict) -> dict:
# normalize first
norm = MRFLabelNormalization(self.min_, self.max_)
sample = norm(sample)
maps = pymia_tfm.check_and_return(sample[pymia_def.KEY_LABELS],
np.ndarray)
mask = pymia_tfm.check_and_return(sample[self.mask], np.ndarray)
mask = np.concatenate([mask] * maps.shape[-1], -1)
maps[mask == 0] = 0
sample[pymia_def.KEY_LABELS] = maps
return sample
def __call__(self, sample: dict) -> dict:
for entry in self.entries:
if entry not in sample:
raise ValueError(
pymia_tfm.ENTRY_NOT_EXTRACTED_ERR_MSG.format(entry))
np_entry = pymia_tfm.check_and_return(sample[entry], np.ndarray)
frame = np_entry[..., self.temporal_idx, :]
if len(frame.shape) == 3: # without batch dimension
shape = frame[..., 0].shape # spatial dimension
# permute real and imaginary independently
frame[..., 0] = np.random.permutation(
frame[..., 0].reshape(-1)).reshape(shape)
frame[..., 1] = np.random.permutation(
frame[..., 1].reshape(-1)).reshape(shape)
else:
# we have the batch dimension
shape = frame[0, ..., 0].shape # spatial dimension
for batch_idx in range(frame.shape[0]):
# permute real and imaginary independently
frame[batch_idx, ..., 0] = np.random.permutation(
frame[batch_idx, ..., 0].reshape(-1)).reshape(shape)
frame[batch_idx, ..., 1] = np.random.permutation(
frame[batch_idx, ..., 1].reshape(-1)).reshape(shape)
np_entry[..., self.temporal_idx, :] = frame
sample[entry] = np_entry
return sample
def __call__(self, sample: dict) -> dict:
maps = pymia_tfm.check_and_return(sample[pymia_def.KEY_LABELS],
np.ndarray)
for idx, entry in enumerate(self.min_.keys()):
maps[..., idx] = (maps[..., idx] - self.min_[entry]) / (
self.max_[entry] - self.min_[entry]) # normalize to 0..1
sample[pymia_def.KEY_LABELS] = maps
return sample
def __call__(self, sample: dict) -> dict:
for entry in self.entries:
if entry not in sample:
raise ValueError(
pymia_tfm.ENTRY_NOT_EXTRACTED_ERR_MSG.format(entry))
np_entry = pymia_tfm.check_and_return(sample[entry], np.ndarray)
np_entry[..., self.temporal_idx, :] = 0
sample[entry] = np_entry
return sample
def __call__(self, sample: dict) -> dict:
# unsqueeze samples to be 4-D tensors, as required by TorchIO
for entry in self.entries:
if entry not in sample:
if tfm.raise_error_if_entry_not_extracted:
raise ValueError(tfm.ENTRY_NOT_EXTRACTED_ERR_MSG.format(entry))
continue
np_entry = tfm.check_and_return(sample[entry], np.ndarray)
sample[entry] = np.expand_dims(np_entry, -1)
# apply TorchIO transforms
for t in self.transforms:
sample = t(sample)
# squeeze samples back to original format
for entry in self.entries:
np_entry = tfm.check_and_return(sample[entry].numpy(), np.ndarray)
sample[entry] = np_entry.squeeze(-1)
return sample
def __call__(self, sample: dict) -> dict:
# unsqueeze samples to add a batch dimensions, as required by batchgenerators
for entry in self.entries:
if entry not in sample:
if tfm.raise_error_if_entry_not_extracted:
raise ValueError(tfm.ENTRY_NOT_EXTRACTED_ERR_MSG.format(entry))
continue
np_entry = tfm.check_and_return(sample[entry], np.ndarray)
sample[entry] = np.expand_dims(np_entry, 0)
# apply batchgenerators transforms
for t in self.transforms:
sample = t(**sample)
# squeeze samples back to original format
for entry in self.entries:
np_entry = tfm.check_and_return(sample[entry], np.ndarray)
sample[entry] = np_entry.squeeze(0)
return sample